import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
np.c_[data['data'], data['target']][0]
array([1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,
3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,
8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,
3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,
1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01,
0.000e+00])
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
data
{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,
1.189e-01],
[2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,
8.902e-02],
[1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,
8.758e-02],
...,
[1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,
7.820e-02],
[2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,
1.240e-01],
[7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,
7.039e-02]]),
'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,
1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),
'frame': None,
'target_names': array(['malignant', 'benign'], dtype='<U9'),
'DESCR': '.. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 569\n\n :Number of Attributes: 30 numeric, predictive attributes and the class\n\n :Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry\n - fractal dimension ("coastline approximation" - 1)\n\n The mean, standard error, and "worst" or largest (mean of the three\n worst/largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 0 is Mean Radius, field\n 10 is Radius SE, field 20 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n :Summary Statistics:\n\n ===================================== ====== ======\n Min Max\n ===================================== ====== ======\n radius (mean): 6.981 28.11\n texture (mean): 9.71 39.28\n perimeter (mean): 43.79 188.5\n area (mean): 143.5 2501.0\n smoothness (mean): 0.053 0.163\n compactness (mean): 0.019 0.345\n concavity (mean): 0.0 0.427\n concave points (mean): 0.0 0.201\n symmetry (mean): 0.106 0.304\n fractal dimension (mean): 0.05 0.097\n radius (standard error): 0.112 2.873\n texture (standard error): 0.36 4.885\n perimeter (standard error): 0.757 21.98\n area (standard error): 6.802 542.2\n smoothness (standard error): 0.002 0.031\n compactness (standard error): 0.002 0.135\n concavity (standard error): 0.0 0.396\n concave points (standard error): 0.0 0.053\n symmetry (standard error): 0.008 0.079\n fractal dimension (standard error): 0.001 0.03\n radius (worst): 7.93 36.04\n texture (worst): 12.02 49.54\n perimeter (worst): 50.41 251.2\n area (worst): 185.2 4254.0\n smoothness (worst): 0.071 0.223\n compactness (worst): 0.027 1.058\n concavity (worst): 0.0 1.252\n concave points (worst): 0.0 0.291\n symmetry (worst): 0.156 0.664\n fractal dimension (worst): 0.055 0.208\n ===================================== ====== ======\n\n :Missing Attribute Values: None\n\n :Class Distribution: 212 - Malignant, 357 - Benign\n\n :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n :Donor: Nick Street\n\n :Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass. They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree\nConstruction Via Linear Programming." Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree. Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: "Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. topic:: References\n\n - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n San Jose, CA, 1993.\n - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n July-August 1995.\n - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n 163-171.',
'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness', 'mean compactness', 'mean concavity',
'mean concave points', 'mean symmetry', 'mean fractal dimension',
'radius error', 'texture error', 'perimeter error', 'area error',
'smoothness error', 'compactness error', 'concavity error',
'concave points error', 'symmetry error',
'fractal dimension error', 'worst radius', 'worst texture',
'worst perimeter', 'worst area', 'worst smoothness',
'worst compactness', 'worst concavity', 'worst concave points',
'worst symmetry', 'worst fractal dimension'], dtype='<U23'),
'filename': 'breast_cancer.csv',
'data_module': 'sklearn.datasets.data'}
cancer = pd.DataFrame(np.c_[data['data'], data['target']], columns = np.append(data['feature_names'],['target']))
df = pd.read_csv('heart.csv')
cancer.head()
| mean radius | mean texture | mean perimeter | mean area | mean smoothness | mean compactness | mean concavity | mean concave points | mean symmetry | mean fractal dimension | ... | worst texture | worst perimeter | worst area | worst smoothness | worst compactness | worst concavity | worst concave points | worst symmetry | worst fractal dimension | target | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 17.99 | 10.38 | 122.80 | 1001.0 | 0.11840 | 0.27760 | 0.3001 | 0.14710 | 0.2419 | 0.07871 | ... | 17.33 | 184.60 | 2019.0 | 0.1622 | 0.6656 | 0.7119 | 0.2654 | 0.4601 | 0.11890 | 0.0 |
| 1 | 20.57 | 17.77 | 132.90 | 1326.0 | 0.08474 | 0.07864 | 0.0869 | 0.07017 | 0.1812 | 0.05667 | ... | 23.41 | 158.80 | 1956.0 | 0.1238 | 0.1866 | 0.2416 | 0.1860 | 0.2750 | 0.08902 | 0.0 |
| 2 | 19.69 | 21.25 | 130.00 | 1203.0 | 0.10960 | 0.15990 | 0.1974 | 0.12790 | 0.2069 | 0.05999 | ... | 25.53 | 152.50 | 1709.0 | 0.1444 | 0.4245 | 0.4504 | 0.2430 | 0.3613 | 0.08758 | 0.0 |
| 3 | 11.42 | 20.38 | 77.58 | 386.1 | 0.14250 | 0.28390 | 0.2414 | 0.10520 | 0.2597 | 0.09744 | ... | 26.50 | 98.87 | 567.7 | 0.2098 | 0.8663 | 0.6869 | 0.2575 | 0.6638 | 0.17300 | 0.0 |
| 4 | 20.29 | 14.34 | 135.10 | 1297.0 | 0.10030 | 0.13280 | 0.1980 | 0.10430 | 0.1809 | 0.05883 | ... | 16.67 | 152.20 | 1575.0 | 0.1374 | 0.2050 | 0.4000 | 0.1625 | 0.2364 | 0.07678 | 0.0 |
5 rows × 31 columns
data['target_names']
array(['malignant', 'benign'], dtype='<U9')
cancer['target_names'] = cancer['target'].replace(to_replace=[0,1], value=['malignant','benign'])
cancer.shape
(569, 32)
cancer.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 569 entries, 0 to 568 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 mean radius 569 non-null float64 1 mean texture 569 non-null float64 2 mean perimeter 569 non-null float64 3 mean area 569 non-null float64 4 mean smoothness 569 non-null float64 5 mean compactness 569 non-null float64 6 mean concavity 569 non-null float64 7 mean concave points 569 non-null float64 8 mean symmetry 569 non-null float64 9 mean fractal dimension 569 non-null float64 10 radius error 569 non-null float64 11 texture error 569 non-null float64 12 perimeter error 569 non-null float64 13 area error 569 non-null float64 14 smoothness error 569 non-null float64 15 compactness error 569 non-null float64 16 concavity error 569 non-null float64 17 concave points error 569 non-null float64 18 symmetry error 569 non-null float64 19 fractal dimension error 569 non-null float64 20 worst radius 569 non-null float64 21 worst texture 569 non-null float64 22 worst perimeter 569 non-null float64 23 worst area 569 non-null float64 24 worst smoothness 569 non-null float64 25 worst compactness 569 non-null float64 26 worst concavity 569 non-null float64 27 worst concave points 569 non-null float64 28 worst symmetry 569 non-null float64 29 worst fractal dimension 569 non-null float64 30 target 569 non-null float64 31 target_names 569 non-null object dtypes: float64(31), object(1) memory usage: 142.4+ KB
cancer.corr()['target'].sort_values(ascending=False)
target 1.000000 smoothness error 0.067016 mean fractal dimension 0.012838 texture error 0.008303 symmetry error 0.006522 fractal dimension error -0.077972 concavity error -0.253730 compactness error -0.292999 worst fractal dimension -0.323872 mean symmetry -0.330499 mean smoothness -0.358560 concave points error -0.408042 mean texture -0.415185 worst symmetry -0.416294 worst smoothness -0.421465 worst texture -0.456903 area error -0.548236 perimeter error -0.556141 radius error -0.567134 worst compactness -0.590998 mean compactness -0.596534 worst concavity -0.659610 mean concavity -0.696360 mean area -0.708984 mean radius -0.730029 worst area -0.733825 mean perimeter -0.742636 worst radius -0.776454 mean concave points -0.776614 worst perimeter -0.782914 worst concave points -0.793566 Name: target, dtype: float64
plt.figure(figsize=(30,20))
sns.heatmap(cancer.corr(), annot=True, fmt='.0%')
plt.show()
cancer.describe()
| mean radius | mean texture | mean perimeter | mean area | mean smoothness | mean compactness | mean concavity | mean concave points | mean symmetry | mean fractal dimension | ... | worst texture | worst perimeter | worst area | worst smoothness | worst compactness | worst concavity | worst concave points | worst symmetry | worst fractal dimension | target | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | ... | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 | 569.000000 |
| mean | 14.127292 | 19.289649 | 91.969033 | 654.889104 | 0.096360 | 0.104341 | 0.088799 | 0.048919 | 0.181162 | 0.062798 | ... | 25.677223 | 107.261213 | 880.583128 | 0.132369 | 0.254265 | 0.272188 | 0.114606 | 0.290076 | 0.083946 | 0.627417 |
| std | 3.524049 | 4.301036 | 24.298981 | 351.914129 | 0.014064 | 0.052813 | 0.079720 | 0.038803 | 0.027414 | 0.007060 | ... | 6.146258 | 33.602542 | 569.356993 | 0.022832 | 0.157336 | 0.208624 | 0.065732 | 0.061867 | 0.018061 | 0.483918 |
| min | 6.981000 | 9.710000 | 43.790000 | 143.500000 | 0.052630 | 0.019380 | 0.000000 | 0.000000 | 0.106000 | 0.049960 | ... | 12.020000 | 50.410000 | 185.200000 | 0.071170 | 0.027290 | 0.000000 | 0.000000 | 0.156500 | 0.055040 | 0.000000 |
| 25% | 11.700000 | 16.170000 | 75.170000 | 420.300000 | 0.086370 | 0.064920 | 0.029560 | 0.020310 | 0.161900 | 0.057700 | ... | 21.080000 | 84.110000 | 515.300000 | 0.116600 | 0.147200 | 0.114500 | 0.064930 | 0.250400 | 0.071460 | 0.000000 |
| 50% | 13.370000 | 18.840000 | 86.240000 | 551.100000 | 0.095870 | 0.092630 | 0.061540 | 0.033500 | 0.179200 | 0.061540 | ... | 25.410000 | 97.660000 | 686.500000 | 0.131300 | 0.211900 | 0.226700 | 0.099930 | 0.282200 | 0.080040 | 1.000000 |
| 75% | 15.780000 | 21.800000 | 104.100000 | 782.700000 | 0.105300 | 0.130400 | 0.130700 | 0.074000 | 0.195700 | 0.066120 | ... | 29.720000 | 125.400000 | 1084.000000 | 0.146000 | 0.339100 | 0.382900 | 0.161400 | 0.317900 | 0.092080 | 1.000000 |
| max | 28.110000 | 39.280000 | 188.500000 | 2501.000000 | 0.163400 | 0.345400 | 0.426800 | 0.201200 | 0.304000 | 0.097440 | ... | 49.540000 | 251.200000 | 4254.000000 | 0.222600 | 1.058000 | 1.252000 | 0.291000 | 0.663800 | 0.207500 | 1.000000 |
8 rows × 31 columns
cancer.skew(numeric_only=True).sort_values(ascending=False)
area error 5.447186 concavity error 5.110463 fractal dimension error 3.923969 perimeter error 3.443615 radius error 3.088612 smoothness error 2.314450 symmetry error 2.195133 compactness error 1.902221 worst area 1.859373 worst fractal dimension 1.662579 texture error 1.646444 mean area 1.645732 worst compactness 1.473555 concave points error 1.444678 worst symmetry 1.433928 mean concavity 1.401180 mean fractal dimension 1.304489 mean compactness 1.190123 mean concave points 1.171180 worst concavity 1.150237 worst perimeter 1.128164 worst radius 1.103115 mean perimeter 0.990650 mean radius 0.942380 mean symmetry 0.725609 mean texture 0.650450 worst texture 0.498321 worst concave points 0.492616 mean smoothness 0.456324 worst smoothness 0.415426 target -0.528461 dtype: float64
cancer.iloc[:,30]
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
...
564 0.0
565 0.0
566 0.0
567 0.0
568 1.0
Name: target, Length: 569, dtype: float64
#ax = sns.boxplot(x=Cancer[Cancer.columns[0]])
sns.boxplot(x=cancer[cancer.columns[31]],y=cancer[cancer.columns[0]],data=cancer)
plt.show()
plt.figure(figsize=(21,55))
for i in range(30):
plt.subplot(10,3,i+1)
ax = sns.boxplot(x=cancer[cancer.columns[31]],y=cancer[cancer.columns[0]],data=cancer)
#ax = sns.swarmplot(x=cancer[cancer.columns[31]],y=cancer[cancer.columns[0]],color=".35", data=cancer)
#ax = sns.swarmplot(y=Cancer[Cancer.columns[i]], color=".30")
plt.title(cancer.columns[i], fontsize=15)
plt.show()
from scipy import stats
z = stats.zscore(cancer['mean radius'])
z_abs = np.abs(z)
np.where(z_abs > 3)
(array([ 82, 180, 212, 352, 461], dtype=int64),)
Q1 = np.percentile(cancer['mean radius'], 20, interpolation = 'midpoint')
Q3 = np.percentile(cancer['mean radius'], 80, interpolation = 'midpoint')
IQR = Q3 - Q1
IQR
5.705000000000002
upper_bound = cancer['mean radius'] >= (Q3+1.5*IQR)
lower_bound = cancer['mean radius'] <= (Q1-1.5*IQR)
np.where(upper_bound)
(array([180, 212, 352, 461], dtype=int64),)
np.where(lower_bound)
(array([], dtype=int64),)
upper_points = np.where(upper_bound)
#Cancer.drop(upper_points[0], inplace=True)
for i in range(30):
z = stats.zscore(cancer[cancer.columns[i]])
z_abs = np.abs(z)
#print(Cancer.columns[i], np.where(z_abs > 3))
print(cancer.columns[i],' : ', np.where(z_abs>3))
mean radius : (array([ 82, 180, 212, 352, 461], dtype=int64),)
mean texture : (array([219, 232, 239, 259], dtype=int64),)
mean perimeter : (array([ 82, 122, 180, 212, 352, 461, 521], dtype=int64),)
mean area : (array([ 82, 122, 180, 212, 339, 352, 461, 521], dtype=int64),)
mean smoothness : (array([ 3, 105, 122, 504, 568], dtype=int64),)
mean compactness : (array([ 0, 3, 78, 82, 108, 122, 181, 258, 567], dtype=int64),)
mean concavity : (array([ 78, 82, 108, 122, 152, 202, 352, 461, 567], dtype=int64),)
mean concave points : (array([ 82, 108, 122, 180, 352, 461], dtype=int64),)
mean symmetry : (array([ 25, 60, 78, 122, 146], dtype=int64),)
mean fractal dimension : (array([ 3, 71, 152, 318, 376, 504, 505], dtype=int64),)
radius error : (array([122, 138, 212, 258, 417, 461, 503], dtype=int64),)
texture error : (array([ 12, 83, 122, 192, 416, 473, 557, 559, 561], dtype=int64),)
perimeter error : (array([ 12, 108, 122, 212, 258, 417, 461, 503], dtype=int64),)
area error : (array([122, 212, 265, 368, 461, 503], dtype=int64),)
smoothness error : (array([ 71, 116, 122, 213, 314, 345, 505], dtype=int64),)
compactness error : (array([ 12, 42, 68, 71, 108, 122, 152, 176, 190, 213, 288, 290],
dtype=int64),)
concavity error : (array([ 68, 112, 122, 152, 213, 376], dtype=int64),)
concave points error : (array([ 12, 68, 152, 213, 288, 389], dtype=int64),)
symmetry error : (array([ 3, 42, 78, 119, 122, 138, 146, 190, 212, 314, 351], dtype=int64),)
fractal dimension error : (array([ 12, 71, 112, 151, 152, 176, 213, 290, 376, 388], dtype=int64),)
worst radius : (array([180, 236, 265, 352, 461, 503], dtype=int64),)
worst texture : (array([219, 239, 259, 265], dtype=int64),)
worst perimeter : (array([ 82, 180, 265, 352, 461, 503], dtype=int64),)
worst area : (array([ 23, 180, 236, 265, 339, 352, 368, 461, 503, 521], dtype=int64),)
worst smoothness : (array([ 3, 203, 379], dtype=int64),)
worst compactness : (array([ 3, 9, 14, 42, 72, 181, 190, 379, 562, 567], dtype=int64),)
worst concavity : (array([ 9, 68, 108, 400, 430, 562, 567], dtype=int64),)
worst concave points : (array([], dtype=int64),)
worst symmetry : (array([ 3, 31, 35, 78, 119, 146, 190, 323, 370], dtype=int64),)
worst fractal dimension : (array([ 3, 9, 14, 31, 105, 151, 190, 379, 562], dtype=int64),)
from scipy import stats
index = []
k=[]
for i in range(30):
z = stats.zscore(cancer[cancer.columns[i]])
z_abs = np.abs(z)
#print(Cancer.columns[i], np.where(z_abs > 3))
Q1 = np.percentile(cancer[cancer.columns[i]], 25, interpolation = 'midpoint')
Q3 = np.percentile(cancer[cancer.columns[i]], 75, interpolation = 'midpoint')
IQR = Q3 - Q1
upper_bound = cancer[cancer.columns[i]] > (Q3+3*IQR)
lower_bound = cancer[cancer.columns[i]] <= (Q1-3*IQR)
for j in range(569):
if upper_bound[j]==True:
index.append(j)
for p in range(569):
if lower_bound[p]==True:
k.append(p)
#Cancer.drop(upper_points[0], inplace=True)
#Cancer.reset_index()
#print(i, Cancer.shape)
len(set(index)), len(set(k))
(56, 0)
Cancer=cancer
Cancer.drop(np.array(index), inplace=True)
Cancer.shape,cancer.shape
((513, 32), (513, 32))
Cancer.shape
(513, 32)
Cancer.shape
(513, 32)
plt.figure(figsize=(21,55))
plt.title('Box Plot', fontsize = 25)
for i in range(30):
plt.subplot(10,3,i+1)
ax = sns.boxplot(x=Cancer[Cancer.columns[31]],y=Cancer[Cancer.columns[0]],data=Cancer)
#ax = sns.swarmplot(x=Cancer[Cancer.columns[31]],y=Cancer[Cancer.columns[0]],color=".35", data=Cancer)
plt.title(Cancer.columns[i], fontsize=15)
plt.show()
plt.figure(figsize=(21,55))
for i in range(30):
plt.subplot(10,3,i+1)
ax = sns.violinplot(x=Cancer[Cancer.columns[31]],y=Cancer[Cancer.columns[0]],data=Cancer)
#ax = sns.swarmplot(x=Cancer[cancer.columns[31]],y=cancer[Cancer.columns[0]],color=".35", data=cancer)
#ax = sns.swarmplot(y=Cancer[Cancer.columns[i]], color=".30")
plt.title(Cancer.columns[i], fontsize=15)
plt.show()
sns.pairplot(Cancer, vars = ['worst area', 'mean area', 'area error', 'worst perimeter',
'mean perimeter', 'worst radius', 'mean radius','perimeter error',
'worst texture', 'mean texture'],
hue ='target_names')
plt.show()
Cancer['target_names'].value_counts()
benign 340 malignant 173 Name: target_names, dtype: int64
X = Cancer.drop(columns= ['target','target_names'],axis='columns')
y = Cancer.target
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled
array([[ 2.22607023, -0.32242245, 2.08387925, ..., 1.30686705,
-0.21346267, 0.42289696],
[ 1.93576969, 0.50874698, 1.94405268, ..., 2.25879191,
1.42614976, 0.33167552],
[ 2.13370188, -1.14164978, 2.18995458, ..., 0.9144068 ,
-0.94682351, -0.35248535],
...,
[ 0.91641894, 2.14003639, 0.89776421, ..., 0.56870777,
-1.2242087 , -0.26253086],
[ 2.23596684, 2.43858863, 2.43103488, ..., 2.62620151,
2.32670167, 2.63881798],
[-1.99978191, 1.29453646, -2.0135214 , ..., -1.79941409,
0.01642505, -0.75728052]])
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=2)
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((410, 30), (103, 30), (410,), (103,))
from sklearn.linear_model import LogisticRegression
log_reg= LogisticRegression(random_state= 2)
log_reg.fit(X_train, y_train)
LogisticRegression(random_state=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression(random_state=2)
log_reg.score(X_test, y_test)
0.9611650485436893
from sklearn.svm import SVC
svm = SVC(random_state=2)
svm.fit(X_train, y_train)
SVC(random_state=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
SVC(random_state=2)
svm.score(X_test, y_test)
0.970873786407767
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(random_state=2)
rf.fit(X_train, y_train)
RandomForestClassifier(random_state=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(random_state=2)
rf.score(X_test, y_test)
0.970873786407767
y_predict= rf.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_predict))
precision recall f1-score support
0.0 1.00 0.92 0.96 36
1.0 0.96 1.00 0.98 67
accuracy 0.97 103
macro avg 0.98 0.96 0.97 103
weighted avg 0.97 0.97 0.97 103
confusion_matrix = pd.crosstab(y_test, y_predict, rownames=['Actual'], colnames=['Predicted'])
sns.heatmap(confusion_matrix, annot=True)
plt.show()
from sklearn.decomposition import PCA
pca = PCA()
pca.fit(X)
PCA()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA()
eigenValues=pca.explained_variance_
eigenValues
array([2.80062021e+05, 4.51068272e+03, 1.55400794e+02, 4.63677579e+01,
2.87841924e+01, 2.27964488e+00, 1.54864466e+00, 1.47264573e-01,
8.44270911e-02, 4.89952388e-02, 1.90119682e-02, 4.83729058e-03,
2.16182248e-03, 1.56201245e-03, 5.61616341e-04, 5.00728651e-04,
2.08916018e-04, 1.96705250e-04, 1.48300807e-04, 1.02656363e-04,
6.26465858e-05, 4.83895402e-05, 2.32534778e-05, 1.53549853e-05,
1.05064726e-05, 7.57040366e-06, 2.58229950e-06, 2.19509990e-06,
9.87106163e-07, 2.33812485e-07])
ratio= pca.explained_variance_ratio_
ratio
array([9.83338305e-01, 1.58376601e-02, 5.45634689e-04, 1.62803912e-04,
1.01065467e-04, 8.00416324e-06, 5.43751563e-06, 5.17067238e-07,
2.96435741e-07, 1.72029377e-07, 6.67537732e-08, 1.69844276e-08,
7.59047175e-09, 5.48445187e-09, 1.97191629e-09, 1.75813079e-09,
7.33534387e-10, 6.90660614e-10, 5.20705606e-10, 3.60441353e-10,
2.19961233e-10, 1.69902682e-10, 8.16463272e-11, 5.39135766e-11,
3.68897465e-11, 2.65807833e-11, 9.06682743e-12, 7.70731358e-12,
3.46587266e-12, 8.20949486e-13])
ratio_cum = np.cumsum(ratio)
ratio_cum[2]
0.9997215999517084
#Elbow Method
plt.figure(figsize=(13,8))
plt.plot(ratio,'s--')
plt.title('Elbow Methos')
plt.xlabel('No of components')
plt.ylabel('ratio')
plt.grid(axis ='x')
plt.xticks(list(range(0,len(ratio))), list(range(1, len(ratio)+1)))
plt.show()
plt.figure(figsize=(13,7))
g=sns.lineplot(data=ratio_cum, marker="s", ms=12)
g.set( xlabel = "No. of components", ylabel = "Cumulative sum")
g.set_title("Elbow Method")
plt.show()
pca = PCA(n_components=2)
pca.fit(X)
PCA(n_components=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA(n_components=2)
X_pca = pca.transform(X)
plt.figure(figsize =(13, 8))
g=sns.scatterplot(data=Cancer, x=X_pca[:, 0], y=X_pca[:, 1], hue= 'target_names')
g.set( xlabel = "First Principal Component", ylabel = "Second Principal Component")
g.set_title("Breast Cancer")
plt.show()
X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=2)
from sklearn.ensemble import RandomForestClassifier
model_rf = RandomForestClassifier(random_state=2)
model_rf.fit(X_train_pca, y_train)
RandomForestClassifier(random_state=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(random_state=2)
model_rf.score(X_test_pca, y_test)
0.9611650485436893
X.shape
(513, 30)
for i in range(1,30):
pca = PCA(n_components = i)
X_pca = pca.fit_transform(X)
X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=2)
model_rf = RandomForestClassifier(random_state=2)
model_rf.fit(X_train_pca, y_train)
s=model_rf.score(X_test_pca, y_test)
print(f'n :{i}, accuracy={round(s,3)}')
n :1, accuracy=0.883 n :2, accuracy=0.961 n :3, accuracy=0.961 n :4, accuracy=0.942 n :5, accuracy=0.951 n :6, accuracy=0.951 n :7, accuracy=0.961 n :8, accuracy=0.951 n :9, accuracy=0.961 n :10, accuracy=0.951 n :11, accuracy=0.951 n :12, accuracy=0.951 n :13, accuracy=0.951 n :14, accuracy=0.951 n :15, accuracy=0.951 n :16, accuracy=0.961 n :17, accuracy=0.951 n :18, accuracy=0.981 n :19, accuracy=0.961 n :20, accuracy=0.951 n :21, accuracy=0.951 n :22, accuracy=0.961 n :23, accuracy=0.961 n :24, accuracy=0.971 n :25, accuracy=0.961 n :26, accuracy=0.961 n :27, accuracy=0.971 n :28, accuracy=0.942 n :29, accuracy=0.942
pca = PCA(n_components = 18)
pca.fit(X)
PCA(n_components=18)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA(n_components=18)
X_pca = pca.transform(X)
X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=2)
model_rf = RandomForestClassifier(random_state=2)
model_rf.fit(X_train_pca, y_train)
RandomForestClassifier(random_state=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifier(random_state=2)
model_rf.score(X_test_pca, y_test)
0.9805825242718447
y_predict_pca= model_rf.predict(X_test_pca)
y_predict_pca
array([0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0., 0.,
1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0.,
1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0.,
1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 1., 0., 1., 1., 1.,
0., 1., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 1., 1., 0., 0.,
1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0., 0., 0., 1., 0., 1.,
1.])
print(classification_report(y_test, y_predict_pca))
precision recall f1-score support
0.0 0.97 0.97 0.97 36
1.0 0.99 0.99 0.99 67
accuracy 0.98 103
macro avg 0.98 0.98 0.98 103
weighted avg 0.98 0.98 0.98 103
confusion_matrix = pd.crosstab(y_test, y_predict_pca, rownames=['Actual'], colnames=['Predicted'])
sns.heatmap(confusion_matrix, annot=True)
plt.show()
